While the focus of the paper is on semantic segmentation, we explore recall loss's application in imbalanced classification. For classification experiments, we benchmark on two popular imbalanced datasets, Place-LT [1]} and iNaturalist2018 [2]}. We compare to the methods that have achieved state-of-the-art performance on either dataset. In addition to recall loss, we employ a Simple Decouple Network (SDN see Appendix REF ) inspired by [3]} to decouple feature learning and classification learning. Please refer to the appendix for more details on the architecture. In the simple decoupled network, the convolution feature backbone is trained with recall loss and the classifier head is trained with inverse frequency loss. We also introduce a baseline model, SDN-CE, which replaces recall loss with a standard cross entropy loss to train the feature backbone.
